
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class        __lowercase		(       _A				,							unittest.TestCase							):
					__magic_name__    :				Dict  						=						KandinskyImgaImgPipeline
					__magic_name__    :				Optional[Any]  						=						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
					__magic_name__    :				Union[str, Any]  						=						[
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					__magic_name__    :				Tuple  						=						[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					__magic_name__    :				Dict  						=						False


					@property
					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									return 3_2


					@property
					def 							lowerCAmelCase_     (						self    )		->				str:





									'''simple docstring'''
									return 3_2


					@property
					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									return self.time_input_dim


					@property
					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									return self.time_input_dim * 4


					@property
					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									return 1_0_0


					@property
					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									A_													=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''    )
									return tokenizer


					@property
					def 							lowerCAmelCase_     (						self    )		->				int:





									'''simple docstring'''
									torch.manual_seed(0    )
									A_													=	MCLIPConfig(
									    numDims=self.cross_attention_dim							,							transformerDimensions=self.text_embedder_hidden_size							,							hidden_size=self.text_embedder_hidden_size							,							intermediate_size=3_7							,							num_attention_heads=4							,							num_hidden_layers=5							,							vocab_size=1_0_0_5							,							)

									A_													=	MultilingualCLIP(a_    )
									A_													=	text_encoder.eval()

									return text_encoder


					@property
					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									torch.manual_seed(0    )

									A_													=	{
									    "in_channels": 4,
									    # Out channels is double in channels because predicts mean and variance
									    "out_channels": 8,
									    "addition_embed_type": "text_image",
									    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
									    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
									    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "layers_per_block": 1,
									    "encoder_hid_dim": self.text_embedder_hidden_size,
									    "encoder_hid_dim_type": "text_image_proj",
									    "cross_attention_dim": self.cross_attention_dim,
									    "attention_head_dim": 4,
									    "resnet_time_scale_shift": "scale_shift",
									    "class_embed_type": None,
									}

									A_													=	UNetaDConditionModel(**a_    )
									return model


					@property
					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									return {
									    "block_out_channels": [3_2, 6_4],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}


					@property
					def 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





									'''simple docstring'''
									torch.manual_seed(0    )
									A_													=	VQModel(**self.dummy_movq_kwargs    )
									return model


					def 							lowerCAmelCase_     (						self    )		->				Any:





									'''simple docstring'''
									A_													=	self.dummy_text_encoder
									A_													=	self.dummy_tokenizer
									A_													=	self.dummy_unet
									A_													=	self.dummy_movq

									A_													=	{
									    "num_train_timesteps": 1_0_0_0,
									    "beta_schedule": "linear",
									    "beta_start": 0.0_00_85,
									    "beta_end": 0.0_12,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									A_													=	DDIMScheduler(**a_    )

									A_													=	{
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components


					def 							lowerCAmelCase_     (						self							,							a__							,							a__=0    )		->				Any:





									'''simple docstring'''
									A_													=	floats_tensor((1, self.cross_attention_dim)							,							rng=random.Random(a_    )    ).to(a_    )
									A_													=	floats_tensor((1, self.cross_attention_dim)							,							rng=random.Random(seed + 1    )    ).to(a_    )
									# create init_image
									A_													=	floats_tensor((1, 3, 6_4, 6_4)							,							rng=random.Random(a_    )    ).to(a_    )
									A_													=	image.cpu().permute(0							,							2							,							3							,							1    )[0]
									A_													=	Image.fromarray(np.uinta(a_    )    ).convert('''RGB'''    ).resize((2_5_6, 2_5_6)    )

									if str(a_    ).startswith('''mps'''    ):
													A_													=	torch.manual_seed(a_    )
									else:
													A_													=	torch.Generator(device=a_    ).manual_seed(a_    )
									A_													=	{
									    "prompt": "horse",
									    "image": init_image,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 1_0,
									    "guidance_scale": 7.0,
									    "strength": 0.2,
									    "output_type": "np",
									}
									return inputs


					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									A_													=	"cpu"

									A_													=	self.get_dummy_components()

									A_													=	self.pipeline_class(**a_    )
									A_													=	pipe.to(a_    )

									pipe.set_progress_bar_config(disable=a_    )

									A_													=	pipe(**self.get_dummy_inputs(a_    )    )
									A_													=	output.images

									A_													=	pipe(
									    **self.get_dummy_inputs(a_    )							,							return_dict=a_							,							)[0]

									A_													=	image[0, -3:, -3:, -1]
									A_													=	image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_													=	np.array(
									    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]    )
									assert (
									    np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
									), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
									), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class        __lowercase		(       unittest.TestCase							):


					def 							lowerCAmelCase_     (						self    )		->				str:





									'''simple docstring'''
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									A_													=	load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinsky/kandinsky_img2img_frog.npy'''    )

									A_													=	load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
									A_													=	"A red cartoon frog, 4k"

									A_													=	KandinskyPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-prior'''							,							torch_dtype=torch.floataa    )
									pipe_prior.to(a_    )

									A_													=	KandinskyImgaImgPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1'''							,							torch_dtype=torch.floataa    )
									A_													=	pipeline.to(a_    )

									pipeline.set_progress_bar_config(disable=a_    )

									A_													=	torch.Generator(device='''cpu'''    ).manual_seed(0    )
									A_													=	pipe_prior(
									    a_							,							generator=a_							,							num_inference_steps=5							,							negative_prompt=''''''							,							).to_tuple()

									A_													=	pipeline(
									    a_							,							image=a_							,							image_embeds=a_							,							negative_image_embeds=a_							,							generator=a_							,							num_inference_steps=1_0_0							,							height=7_6_8							,							width=7_6_8							,							strength=0.2							,							output_type='''np'''							,							)

									A_													=	output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(a_							,							a_    )






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



